Spatiotemporal Interactive Pedestrian Intention Prediction With Illumination Transformation

Predicting pedestrian intention is a desirable capability for the safety of intelligent vehicles (IVs). Recently, deep learning-based methods have achieved decisive progress in improving prediction accuracy. However, the majority of these methods concentrate on drab lighting environments while disregarding the importance of rich pedestrian interactions. To handle the above challenges, this article proposes a pedestrian intention prediction framework which can model complex spatiotemporal interactions with multi-illumination environments. First, a multi-illumination image generation method based on generative adversarial network is constructed to reduce the impact of complex lighting. Second, an interaction model based on 3-D convolution and dense neural network structure is conducted to capture spatial–temporal pedestrians interaction. In addition, the prefusion and postfusion quantify the improvement of different fusion methods on pedestrian intention prediction. Results on JAAD dataset show that the proposed prediction framework can effectively predict pedestrian intentions.